{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NY_OilProduction_EDA_1.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP55K5I9dXaszfFaIKvtI8n",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/NY_OilProduction_EDA_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VIbbpopkvrLO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mOUY3V5xhC3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('NYC_production_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVq6LLqVxrZH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "outputId": "3a74194a-54f2-4ea9-9fa2-41d731343189"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Production Year</th>\n",
              "      <th>Production Date Entered</th>\n",
              "      <th>Operator</th>\n",
              "      <th>County</th>\n",
              "      <th>Town</th>\n",
              "      <th>Field</th>\n",
              "      <th>Producing Formation</th>\n",
              "      <th>Active Oil Wells</th>\n",
              "      <th>Inactive Oil Wells</th>\n",
              "      <th>Active Gas Wells</th>\n",
              "      <th>Inactive Gas Wells</th>\n",
              "      <th>Injection Wells</th>\n",
              "      <th>Disposal Wells</th>\n",
              "      <th>Self-use Well</th>\n",
              "      <th>Oil Produced, bbl</th>\n",
              "      <th>Gas Produced, Mcf</th>\n",
              "      <th>Water produced, bbl</th>\n",
              "      <th>Taxable Gas, Mcf</th>\n",
              "      <th>Purchaser Codes</th>\n",
              "      <th>Location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1995</td>\n",
              "      <td>12/03/1996</td>\n",
              "      <td>Buffalo China, Inc.</td>\n",
              "      <td>Erie</td>\n",
              "      <td>BUFFALO</td>\n",
              "      <td>BUFFALO</td>\n",
              "      <td>MEDINA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>YES</td>\n",
              "      <td>0</td>\n",
              "      <td>106</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BUFFALO, NY\\n(42.887691, -78.879374)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1995</td>\n",
              "      <td>03/29/1996</td>\n",
              "      <td>Copper Ridge Oil, Inc.</td>\n",
              "      <td>Steuben</td>\n",
              "      <td>WEST UNION</td>\n",
              "      <td>BEECH HILL-INDEPENDENCE</td>\n",
              "      <td>FULMER VALLEY</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>NO</td>\n",
              "      <td>1229</td>\n",
              "      <td>0</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>OA</td>\n",
              "      <td>WEST UNION, NY\\n(42.094951, -77.725816)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Production Year  ...                                 Location\n",
              "0             1995  ...     BUFFALO, NY\\n(42.887691, -78.879374)\n",
              "1             1995  ...  WEST UNION, NY\\n(42.094951, -77.725816)\n",
              "\n",
              "[2 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "muIbqeuyxtWg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "8d418150-b8db-424c-926e-f9ff82fd8bcb"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Production Year', 'Production Date Entered', 'Operator', 'County',\n",
              "       'Town', 'Field', 'Producing Formation', 'Active Oil Wells',\n",
              "       'Inactive Oil Wells', 'Active Gas Wells', 'Inactive Gas Wells',\n",
              "       'Injection Wells', 'Disposal Wells', 'Self-use Well',\n",
              "       'Oil Produced, bbl', 'Gas Produced, Mcf', 'Water produced, bbl',\n",
              "       'Taxable Gas, Mcf', 'Purchaser Codes', 'Location'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUDfarxdx3mY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_fieldwise =df.groupby('Field').sum()[['Active Oil Wells',\n",
        "       'Inactive Oil Wells', 'Active Gas Wells', 'Inactive Gas Wells',\n",
        "       'Injection Wells', 'Disposal Wells',\n",
        "       'Oil Produced, bbl', 'Gas Produced, Mcf', 'Water produced, bbl']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bU1myT6G1z9G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_fieldwise = df_fieldwise.reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsuylI073dsI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "48b70c8d-a4df-4d21-da0c-d29bea82c7c5"
      },
      "source": [
        "df_fieldwise.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Active Oil Wells</th>\n",
              "      <th>Inactive Oil Wells</th>\n",
              "      <th>Active Gas Wells</th>\n",
              "      <th>Inactive Gas Wells</th>\n",
              "      <th>Injection Wells</th>\n",
              "      <th>Disposal Wells</th>\n",
              "      <th>Oil Produced, bbl</th>\n",
              "      <th>Gas Produced, Mcf</th>\n",
              "      <th>Water produced, bbl</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>229.000000</td>\n",
              "      <td>229.000000</td>\n",
              "      <td>229.000000</td>\n",
              "      <td>229.000000</td>\n",
              "      <td>229.000000</td>\n",
              "      <td>229.000000</td>\n",
              "      <td>2.290000e+02</td>\n",
              "      <td>2.290000e+02</td>\n",
              "      <td>2.290000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>634.161572</td>\n",
              "      <td>171.323144</td>\n",
              "      <td>479.475983</td>\n",
              "      <td>73.366812</td>\n",
              "      <td>164.676856</td>\n",
              "      <td>2.157205</td>\n",
              "      <td>1.045585e+05</td>\n",
              "      <td>2.321356e+06</td>\n",
              "      <td>7.683766e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4062.208758</td>\n",
              "      <td>1261.559589</td>\n",
              "      <td>4142.535459</td>\n",
              "      <td>507.718621</td>\n",
              "      <td>1229.832948</td>\n",
              "      <td>19.989947</td>\n",
              "      <td>6.970628e+05</td>\n",
              "      <td>1.960174e+07</td>\n",
              "      <td>7.553171e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>8.380000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.208500e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>4.311700e+05</td>\n",
              "      <td>8.540000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>39932.000000</td>\n",
              "      <td>16362.000000</td>\n",
              "      <td>62112.000000</td>\n",
              "      <td>7590.000000</td>\n",
              "      <td>11378.000000</td>\n",
              "      <td>226.000000</td>\n",
              "      <td>7.891700e+06</td>\n",
              "      <td>2.942136e+08</td>\n",
              "      <td>9.956594e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Active Oil Wells  ...  Water produced, bbl\n",
              "count        229.000000  ...         2.290000e+02\n",
              "mean         634.161572  ...         7.683766e+05\n",
              "std         4062.208758  ...         7.553171e+06\n",
              "min            0.000000  ...         0.000000e+00\n",
              "25%            0.000000  ...         0.000000e+00\n",
              "50%            0.000000  ...         0.000000e+00\n",
              "75%            0.000000  ...         8.540000e+02\n",
              "max        39932.000000  ...         9.956594e+07\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtLexexM4XNP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "1636f7ab-304b-486b-a2c3-558e36642287"
      },
      "source": [
        "df_fieldwise.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 229 entries, 0 to 228\n",
            "Data columns (total 10 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   Field                229 non-null    object\n",
            " 1   Active Oil Wells     229 non-null    int64 \n",
            " 2   Inactive Oil Wells   229 non-null    int64 \n",
            " 3   Active Gas Wells     229 non-null    int64 \n",
            " 4   Inactive Gas Wells   229 non-null    int64 \n",
            " 5   Injection Wells      229 non-null    int64 \n",
            " 6   Disposal Wells       229 non-null    int64 \n",
            " 7   Oil Produced, bbl    229 non-null    int64 \n",
            " 8   Gas Produced, Mcf    229 non-null    int64 \n",
            " 9   Water produced, bbl  229 non-null    int64 \n",
            "dtypes: int64(9), object(1)\n",
            "memory usage: 18.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0ZZo0tr5SdF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "6c439c52-9260-4347-f767-b3b10446f19d"
      },
      "source": [
        "# df_fieldwise['Active Oil Wells'].sort_values(ascending=False).values # A lot of fields have no oil wells\n",
        "# df_fieldwise['Active Gas Wells'].sort_values(ascending=False).values\n",
        "df_fieldwise['Active Gas Wells'].sort_values(ascending=False).values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([62112,  5902,  4263,  4257,  2926,  1887,  1853,  1775,  1485,\n",
              "        1453,  1444,  1255,  1253,  1127,   833,   829,   820,   806,\n",
              "         728,   703,   622,   608,   475,   398,   387,   380,   359,\n",
              "         345,   312,   296,   288,   278,   276,   275,   262,   246,\n",
              "         229,   223,   219,   199,   198,   166,   164,   158,   156,\n",
              "         155,   152,   150,   145,   141,   126,   116,   114,   106,\n",
              "         105,   103,   101,    93,    92,    90,    89,    88,    87,\n",
              "          85,    79,    75,    75,    74,    68,    62,    60,    58,\n",
              "          54,    51,    48,    48,    47,    46,    46,    46,    41,\n",
              "          40,    39,    39,    38,    38,    37,    35,    35,    34,\n",
              "          33,    32,    32,    31,    29,    29,    28,    27,    25,\n",
              "          23,    22,    22,    22,    21,    21,    20,    19,    19,\n",
              "          19,    18,    18,    18,    17,    16,    16,    15,    15,\n",
              "          15,    15,    15,    15,    14,    14,    14,    12,    12,\n",
              "          11,    11,    11,    11,    10,    10,    10,     9,     9,\n",
              "           9,     9,     9,     9,     9,     9,     9,     8,     8,\n",
              "           8,     8,     8,     7,     7,     6,     6,     6,     6,\n",
              "           6,     6,     6,     6,     5,     5,     5,     5,     5,\n",
              "           5,     5,     5,     5,     5,     5,     5,     4,     4,\n",
              "           4,     4,     4,     4,     4,     3,     3,     3,     2,\n",
              "           2,     2,     2,     2,     2,     2,     2,     2,     1,\n",
              "           1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
              "           1,     1,     1,     1,     0,     0,     0,     0,     0,\n",
              "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "           0,     0,     0,     0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYp0Hy3c5hcc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        },
        "outputId": "c335fdce-eb05-45af-8067-56999136edf6"
      },
      "source": [
        "df_fieldwise[df_fieldwise['Active Oil Wells']>=35]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Field</th>\n",
              "      <th>Active Oil Wells</th>\n",
              "      <th>Inactive Oil Wells</th>\n",
              "      <th>Active Gas Wells</th>\n",
              "      <th>Inactive Gas Wells</th>\n",
              "      <th>Injection Wells</th>\n",
              "      <th>Disposal Wells</th>\n",
              "      <th>Oil Produced, bbl</th>\n",
              "      <th>Gas Produced, Mcf</th>\n",
              "      <th>Water produced, bbl</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ALMA</td>\n",
              "      <td>3310</td>\n",
              "      <td>545</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1753</td>\n",
              "      <td>15</td>\n",
              "      <td>788873</td>\n",
              "      <td>133</td>\n",
              "      <td>3562159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ANDOVER</td>\n",
              "      <td>603</td>\n",
              "      <td>400</td>\n",
              "      <td>23</td>\n",
              "      <td>3</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>31809</td>\n",
              "      <td>50577</td>\n",
              "      <td>30752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>BEECH HILL-INDEPENDENCE</td>\n",
              "      <td>9862</td>\n",
              "      <td>595</td>\n",
              "      <td>829</td>\n",
              "      <td>245</td>\n",
              "      <td>1233</td>\n",
              "      <td>3</td>\n",
              "      <td>1184558</td>\n",
              "      <td>2651569</td>\n",
              "      <td>1312916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>BRADFORD</td>\n",
              "      <td>39932</td>\n",
              "      <td>8511</td>\n",
              "      <td>345</td>\n",
              "      <td>294</td>\n",
              "      <td>10442</td>\n",
              "      <td>6</td>\n",
              "      <td>7891700</td>\n",
              "      <td>6561339</td>\n",
              "      <td>99565944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>BROWNING</td>\n",
              "      <td>633</td>\n",
              "      <td>145</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2757</td>\n",
              "      <td>303</td>\n",
              "      <td>7200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>BUSTI</td>\n",
              "      <td>10097</td>\n",
              "      <td>683</td>\n",
              "      <td>806</td>\n",
              "      <td>151</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1302727</td>\n",
              "      <td>1902421</td>\n",
              "      <td>53128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>CERES</td>\n",
              "      <td>80</td>\n",
              "      <td>22</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5169</td>\n",
              "      <td>5134</td>\n",
              "      <td>16265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>CHIPMUNK</td>\n",
              "      <td>23004</td>\n",
              "      <td>4411</td>\n",
              "      <td>164</td>\n",
              "      <td>61</td>\n",
              "      <td>11378</td>\n",
              "      <td>0</td>\n",
              "      <td>6087275</td>\n",
              "      <td>833795</td>\n",
              "      <td>56057400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>CLARKSVILLE</td>\n",
              "      <td>297</td>\n",
              "      <td>165</td>\n",
              "      <td>29</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35654</td>\n",
              "      <td>455732</td>\n",
              "      <td>127294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>CLYMER</td>\n",
              "      <td>66</td>\n",
              "      <td>6</td>\n",
              "      <td>275</td>\n",
              "      <td>166</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>213901</td>\n",
              "      <td>3527698</td>\n",
              "      <td>101844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>CORBIN HILL</td>\n",
              "      <td>41</td>\n",
              "      <td>55</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2052</td>\n",
              "      <td>1336</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>ELLERY</td>\n",
              "      <td>212</td>\n",
              "      <td>77</td>\n",
              "      <td>166</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>401649</td>\n",
              "      <td>1817250</td>\n",
              "      <td>247299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>FARMERSVILLE</td>\n",
              "      <td>40</td>\n",
              "      <td>33</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3971</td>\n",
              "      <td>208</td>\n",
              "      <td>167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>FIVE MILE</td>\n",
              "      <td>5678</td>\n",
              "      <td>847</td>\n",
              "      <td>51</td>\n",
              "      <td>65</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>882269</td>\n",
              "      <td>370409</td>\n",
              "      <td>113408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>FORD'S BROOK</td>\n",
              "      <td>3232</td>\n",
              "      <td>1355</td>\n",
              "      <td>21</td>\n",
              "      <td>6</td>\n",
              "      <td>1095</td>\n",
              "      <td>0</td>\n",
              "      <td>106919</td>\n",
              "      <td>25084</td>\n",
              "      <td>2674298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>FOUR MILE</td>\n",
              "      <td>685</td>\n",
              "      <td>247</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>180395</td>\n",
              "      <td>576</td>\n",
              "      <td>16897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>FULMER VALLEY</td>\n",
              "      <td>4720</td>\n",
              "      <td>1826</td>\n",
              "      <td>68</td>\n",
              "      <td>49</td>\n",
              "      <td>473</td>\n",
              "      <td>0</td>\n",
              "      <td>148554</td>\n",
              "      <td>113503</td>\n",
              "      <td>10062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>GERRY-CHARLOTTE</td>\n",
              "      <td>230</td>\n",
              "      <td>52</td>\n",
              "      <td>229</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>722775</td>\n",
              "      <td>4738243</td>\n",
              "      <td>143129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>LAKESHORE</td>\n",
              "      <td>163</td>\n",
              "      <td>89</td>\n",
              "      <td>62112</td>\n",
              "      <td>7590</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>137478</td>\n",
              "      <td>294213573</td>\n",
              "      <td>1531604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>MARSH</td>\n",
              "      <td>1233</td>\n",
              "      <td>1286</td>\n",
              "      <td>288</td>\n",
              "      <td>15</td>\n",
              "      <td>181</td>\n",
              "      <td>0</td>\n",
              "      <td>101771</td>\n",
              "      <td>271858</td>\n",
              "      <td>95557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>NILE</td>\n",
              "      <td>143</td>\n",
              "      <td>138</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4017</td>\n",
              "      <td>22</td>\n",
              "      <td>2625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>NORTH HARMONY</td>\n",
              "      <td>217</td>\n",
              "      <td>71</td>\n",
              "      <td>296</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>512399</td>\n",
              "      <td>3146821</td>\n",
              "      <td>164039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>OLEAN</td>\n",
              "      <td>394</td>\n",
              "      <td>61</td>\n",
              "      <td>37</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29702</td>\n",
              "      <td>77304</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>RICHBURG</td>\n",
              "      <td>38058</td>\n",
              "      <td>16362</td>\n",
              "      <td>1444</td>\n",
              "      <td>543</td>\n",
              "      <td>10292</td>\n",
              "      <td>201</td>\n",
              "      <td>2888186</td>\n",
              "      <td>4143242</td>\n",
              "      <td>7831576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>RUSHFORD</td>\n",
              "      <td>36</td>\n",
              "      <td>17</td>\n",
              "      <td>15</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6701</td>\n",
              "      <td>26916</td>\n",
              "      <td>705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>SCIO</td>\n",
              "      <td>1986</td>\n",
              "      <td>998</td>\n",
              "      <td>46</td>\n",
              "      <td>21</td>\n",
              "      <td>766</td>\n",
              "      <td>1</td>\n",
              "      <td>132646</td>\n",
              "      <td>189821</td>\n",
              "      <td>1104430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>UNNAMED</td>\n",
              "      <td>78</td>\n",
              "      <td>14</td>\n",
              "      <td>126</td>\n",
              "      <td>309</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>12650</td>\n",
              "      <td>706273</td>\n",
              "      <td>214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>WIRT</td>\n",
              "      <td>35</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>387</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       Field  ...  Water produced, bbl\n",
              "8                       ALMA  ...              3562159\n",
              "9                    ANDOVER  ...                30752\n",
              "20   BEECH HILL-INDEPENDENCE  ...              1312916\n",
              "30                  BRADFORD  ...             99565944\n",
              "33                  BROWNING  ...                 7200\n",
              "38                     BUSTI  ...                53128\n",
              "47                     CERES  ...                16265\n",
              "50                  CHIPMUNK  ...             56057400\n",
              "53               CLARKSVILLE  ...               127294\n",
              "54                    CLYMER  ...               101844\n",
              "63               CORBIN HILL  ...                    0\n",
              "80                    ELLERY  ...               247299\n",
              "86              FARMERSVILLE  ...                  167\n",
              "89                 FIVE MILE  ...               113408\n",
              "91              FORD'S BROOK  ...              2674298\n",
              "92                 FOUR MILE  ...                16897\n",
              "96             FULMER VALLEY  ...                10062\n",
              "99           GERRY-CHARLOTTE  ...               143129\n",
              "130                LAKESHORE  ...              1531604\n",
              "137                    MARSH  ...                95557\n",
              "146                     NILE  ...                 2625\n",
              "150            NORTH HARMONY  ...               164039\n",
              "154                    OLEAN  ...                    0\n",
              "169                 RICHBURG  ...              7831576\n",
              "174                 RUSHFORD  ...                  705\n",
              "178                     SCIO  ...              1104430\n",
              "208                  UNNAMED  ...                  214\n",
              "224                     WIRT  ...                    0\n",
              "\n",
              "[28 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShUg31ua6vr1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_producingFields = df_fieldwise[(df_fieldwise['Active Gas Wells']>0)&(df_fieldwise['Active Oil Wells']>0)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VE0HDlno8EfH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "52967853-df43-4caa-a7ed-50d4ff0a3ccf"
      },
      "source": [
        "df_producingFields.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Field</th>\n",
              "      <th>Active Oil Wells</th>\n",
              "      <th>Inactive Oil Wells</th>\n",
              "      <th>Active Gas Wells</th>\n",
              "      <th>Inactive Gas Wells</th>\n",
              "      <th>Injection Wells</th>\n",
              "      <th>Disposal Wells</th>\n",
              "      <th>Oil Produced, bbl</th>\n",
              "      <th>Gas Produced, Mcf</th>\n",
              "      <th>Water produced, bbl</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ANDOVER</td>\n",
              "      <td>603</td>\n",
              "      <td>400</td>\n",
              "      <td>23</td>\n",
              "      <td>3</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>31809</td>\n",
              "      <td>50577</td>\n",
              "      <td>30752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>BEECH HILL-INDEPENDENCE</td>\n",
              "      <td>9862</td>\n",
              "      <td>595</td>\n",
              "      <td>829</td>\n",
              "      <td>245</td>\n",
              "      <td>1233</td>\n",
              "      <td>3</td>\n",
              "      <td>1184558</td>\n",
              "      <td>2651569</td>\n",
              "      <td>1312916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>BIXBY HILL</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12073</td>\n",
              "      <td>7361</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>BRADFORD</td>\n",
              "      <td>39932</td>\n",
              "      <td>8511</td>\n",
              "      <td>345</td>\n",
              "      <td>294</td>\n",
              "      <td>10442</td>\n",
              "      <td>6</td>\n",
              "      <td>7891700</td>\n",
              "      <td>6561339</td>\n",
              "      <td>99565944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>BRANT-EDEN</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>4263</td>\n",
              "      <td>294</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10824685</td>\n",
              "      <td>77423</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Field  ...  Water produced, bbl\n",
              "9                   ANDOVER  ...                30752\n",
              "20  BEECH HILL-INDEPENDENCE  ...              1312916\n",
              "28               BIXBY HILL  ...                    1\n",
              "30                 BRADFORD  ...             99565944\n",
              "32               BRANT-EDEN  ...                77423\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XTgGnBf8zZP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "ef72955f-55e3-4223-a1eb-ef12dc6f7efc"
      },
      "source": [
        "df_producingFields.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Field', 'Active Oil Wells', 'Inactive Oil Wells', 'Active Gas Wells',\n",
              "       'Inactive Gas Wells', 'Injection Wells', 'Disposal Wells',\n",
              "       'Oil Produced, bbl', 'Gas Produced, Mcf', 'Water produced, bbl'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5H-1kO458ZRo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "c2a0b154-e571-4693-a31e-948c2aff454d"
      },
      "source": [
        "sns.scatterplot(y='Oil Produced, bbl',x='Active Oil Wells',data = df_producingFields)\n",
        "# sns.scatterplot(y='Gas Produced, Mcf',x='Active Gas Wells',data = df_producingFields)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6e7492860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ry0OwOYM-4MS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "ecda7b93-0aa6-4d33-a3f5-dd948b20e51f"
      },
      "source": [
        "sns.scatterplot(y='Gas Produced, Mcf',x='Active Gas Wells',data = df_producingFields)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6e74925c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g84yjC1V_Vum",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#So majority of oil wells are between 0-15000 and gas wells bwetween 0-10000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3fmXn0B_udT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_yearwise = df.groupby(by='Production Year').sum()[['Active Oil Wells',\n",
        "       'Inactive Oil Wells', 'Active Gas Wells', 'Inactive Gas Wells',\n",
        "       'Injection Wells', 'Disposal Wells',\n",
        "       'Oil Produced, bbl', 'Gas Produced, Mcf', 'Water produced, bbl']]\n",
        "df_yearwise = df_yearwise.reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooXkdDEB_9RO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "cffb1a19-1b56-49dd-9854-3ad0df7244f3"
      },
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "sns.lineplot(x='Production Year',y ='Active Oil Wells',data=df_yearwise,label='Active Oil Wells' )\n",
        "sns.lineplot(x='Production Year',y ='Active Gas Wells',data=df_yearwise,label='Active Gas Wells')\n",
        "sns.lineplot(x='Production Year',y ='Inactive Oil Wells',data=df_yearwise,label='InActive Oil Wells' )\n",
        "plt.grid()\n",
        "plt.ylabel('#Wells')\n",
        "plt.legend(loc='best')\n",
        "\n",
        "#Conclusion - A visibly evident drop in number of oil wells after 1980\n",
        "#Conclusion - A visibly evident rise in number of oil wells after 1980"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe6e70092b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3r5i72n1BRGQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_yearwise['Liquid produced, bbl'] = df_yearwise['Oil Produced, bbl'] + df_yearwise['Water produced, bbl']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "984-lXr1E6Ax",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "outputId": "b0e7fe0e-e264-4e7a-8514-2a9d8fc349e4"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "# plt.grid()\n",
        "plt.style.use('seaborn-dark-palette')\n",
        "sns.lineplot(data=df_yearwise,x='Production Year',y='Liquid produced, bbl',label = 'Liq. Production(barrels)')\n",
        "sns.lineplot(data=df_yearwise,x='Production Year',y='Oil Produced, bbl',label = 'Oil Production(barrels)')\n",
        "sns.lineplot(data=df_yearwise,x='Production Year',y='Gas Produced, Mcf',label = 'Gas. Production(Mcf)')\n",
        "\n",
        "plt.ylabel('Volume of Fluids (oil,water,liq) Produced')\n",
        "plt.title('Around 1980 - Gas-Prod. shoots up & Liq. Prod declines. ')\n",
        "\n",
        "plt.grid()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgDSEgZdFKVT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plt.style.available"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCtWANdeIrDt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Now let's analyse it date-wise. \n",
        "df['Date'] = pd.to_datetime(df['Production Date Entered'])"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkf4LIpSJyf6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " df = df.sort_values(by='Date')"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SM9uwzzgNP2T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "03335f88-e27d-42f4-fc09-98cdf71cd031"
      },
      "source": [
        "df.head(3)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Production Year</th>\n",
              "      <th>Production Date Entered</th>\n",
              "      <th>Operator</th>\n",
              "      <th>County</th>\n",
              "      <th>Town</th>\n",
              "      <th>Field</th>\n",
              "      <th>Producing Formation</th>\n",
              "      <th>Active Oil Wells</th>\n",
              "      <th>Inactive Oil Wells</th>\n",
              "      <th>Active Gas Wells</th>\n",
              "      <th>Inactive Gas Wells</th>\n",
              "      <th>Injection Wells</th>\n",
              "      <th>Disposal Wells</th>\n",
              "      <th>Self-use Well</th>\n",
              "      <th>Oil Produced, bbl</th>\n",
              "      <th>Gas Produced, Mcf</th>\n",
              "      <th>Water produced, bbl</th>\n",
              "      <th>Taxable Gas, Mcf</th>\n",
              "      <th>Purchaser Codes</th>\n",
              "      <th>Location</th>\n",
              "      <th>Date</th>\n",
              "      <th>Liquid produced, bbl</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14097</th>\n",
              "      <td>1983</td>\n",
              "      <td>01/31/1984</td>\n",
              "      <td>Baker &amp; Odonnell Dev. Co.</td>\n",
              "      <td>Allegany</td>\n",
              "      <td>ALMA</td>\n",
              "      <td>RICHBURG</td>\n",
              "      <td>RICHBURG</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>NO</td>\n",
              "      <td>208</td>\n",
              "      <td>0</td>\n",
              "      <td>925</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ALMA, NY\\n(42.012207, -78.060759)</td>\n",
              "      <td>1984-01-31</td>\n",
              "      <td>1133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15341</th>\n",
              "      <td>1983</td>\n",
              "      <td>01/31/1984</td>\n",
              "      <td>CNG Transmission Corp.</td>\n",
              "      <td>Allegany</td>\n",
              "      <td>ALMA</td>\n",
              "      <td>STATE LINE</td>\n",
              "      <td>ORISKANY</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>INACTIVE</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ALMA, NY\\n(42.012207, -78.060759)</td>\n",
              "      <td>1984-01-31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15625</th>\n",
              "      <td>1983</td>\n",
              "      <td>01/31/1984</td>\n",
              "      <td>Patrick Petroleum Corp. of Michigan</td>\n",
              "      <td>Allegany</td>\n",
              "      <td>SCIO</td>\n",
              "      <td>GORDON BROOK</td>\n",
              "      <td>ORISKANY</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NO</td>\n",
              "      <td>0</td>\n",
              "      <td>18048</td>\n",
              "      <td>2000</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SCIO, NY\\n(42.171429, -77.97865)</td>\n",
              "      <td>1984-01-31</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Production Year Production Date Entered  ...       Date Liquid produced, bbl\n",
              "14097             1983              01/31/1984  ... 1984-01-31                 1133\n",
              "15341             1983              01/31/1984  ... 1984-01-31                    0\n",
              "15625             1983              01/31/1984  ... 1984-01-31                 2000\n",
              "\n",
              "[3 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swBOXUs5KrCj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Liquid produced, bbl'] = df['Oil Produced, bbl'] + df['Water produced, bbl']"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIVN9CoFM7xd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "c1ab7e8e-6d04-406a-df53-20a62ea447be"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Production Year', 'Production Date Entered', 'Operator', 'County',\n",
              "       'Town', 'Field', 'Producing Formation', 'Active Oil Wells',\n",
              "       'Inactive Oil Wells', 'Active Gas Wells', 'Inactive Gas Wells',\n",
              "       'Injection Wells', 'Disposal Wells', 'Self-use Well',\n",
              "       'Oil Produced, bbl', 'Gas Produced, Mcf', 'Water produced, bbl',\n",
              "       'Taxable Gas, Mcf', 'Purchaser Codes', 'Location', 'Date',\n",
              "       'Liquid produced, bbl'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXHyMF_MMloC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_datewise = df.groupby(by='Date').sum()[['Active Oil Wells',\n",
        "       'Inactive Oil Wells', 'Active Gas Wells', 'Inactive Gas Wells',\n",
        "       'Injection Wells', 'Disposal Wells',\n",
        "       'Oil Produced, bbl', 'Gas Produced, Mcf', 'Water produced, bbl']]\n"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkrJfJ-mNlsr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_datewise.head(4)\n",
        "df_datewise.reset_index(inplace=True)"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxEYz_nMN-Fd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_datewise['Liquid produced, bbl'] = df_datewise['Oil Produced, bbl'] + df_datewise['Water produced, bbl']"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQ8K51qdL14P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "outputId": "c7678c3e-03f8-4bec-b042-756015582685"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "# plt.grid()\n",
        "\n",
        "sns.lineplot(data=df_datewise,x='Date',y='Liquid produced, bbl',label = 'Liq. Production(barrels)')\n",
        "sns.lineplot(data=df_datewise,x='Date',y='Oil Produced, bbl',label = 'Oil Production(barrels)')\n",
        "sns.lineplot(data=df_datewise,x='Date',y='Gas Produced, Mcf',label = 'Gas. Production(Mcf)')\n",
        "\n",
        "plt.ylabel('Volume of Fluids (oil,water,liq) Produced')\n",
        "# plt.title('Around 1980 - Gas-Prod. shoots up & Liq. Prod declines. ')\n",
        "\n",
        "plt.grid()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A3ng30BkMFyl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#You can see an observable Production peak somewhere from 1992-1996\n",
        "#However don't get confused with the peaks. Don't compare the magnitudes \n",
        "# since liquids are in barrels and gas is in Mcf"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8-PLdLUPpEz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# sns.(data=df_datewise,x='Date',y='Liquid produced, bbl',label = 'Liq. Production(barrels)')"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHnO9m73P91S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#### End of Part 1 ####"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPw55uQXRJzT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}